Automated fault analysis and diagnosis using high-frequency and maintenance data from distribution networks

Fault analysis based on high-resolution data acquisition is growing in use as it offers a more complete picture of faults which provides an opportunity to deal with failures more effectively. However, with increased volume of data collected, it becomes impossible for engineers to interpret every fault instance. A machine learning approach to classification should be the solution to this, but it is time-consuming to manually label faults for training and validation making data-driven approaches impossible to transfer into practical implementation. A solution to this is to unify fault analysis with maintenance report analysis to automate the generation of training labels. This paper outlines how a fully automatic fault detection and diagnostic approach based around power quality waveform analysis can be used to improve situational awareness on distribution networks. The methodology is illustrated using operational case study data and realistic simulations to demonstrate the diagnostic functionality as well as the practical benefit. In particular, classification accuracy is shown to approach that of expert labelled fault data.